base-rate counterfactual dial · meter band gauge

Invert an Alert Probability Without Losing the Population Base Rate

“Ninety percent sensitive” is not “ninety percent of alerts are right.” Build the two populations behind an alert and see how their sizes change the conditional probability.

1 · Detector code | sensitivity percent | specificity percent

FIRST-LOAD

HYPOTHESIS / PROTOTYPE — checkout unavailable. Calculation is local. A draft is saved automatically in this browser profile when storage is available; Reset to sample clears it. Optional Pro history stores only five summaries and has its own deletion control. State links encode your inputs and can remain in browser history, clipboard or recipients’ records; share only non-sensitive rows. Optional external AI formatting leaves this device. The required site analytics beacon reports page activity; shared URLs contain encoded inputs. Do not treat an encoded URL as private. The calculator has no input-collection endpoint.

Data note: This base-rate counterfactual dial calculates in the tab from Detector code | sensitivity percent | specificity percent. No input-collection endpoint, AI request or file upload is built into it. Drafts may be saved locally; explicit input-state links and optional external formatting can disclose the records. Use non-sensitive codes and clear the draft when finished.

Perspective: Before: sensitivity sounded like the chance an alert was correct. After: the positive and negative populations build the actual alert denominator before its probability is inverted.

2 · Read the base-rate counterfactual dial

Classroom conditional-probability model only; no medical, hiring, legal or real-person screening decision. Verify definitions and assumptions with a qualified teacher or statistician before using the method beyond the invented exercise.

Paid layer: base-rate counterfactual dial filing layer

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one full, unwatermarked base-rate counterfactual dial App execution
three invented readings and unlimited calculations in the paired article demo
copy the complete working and boundary; voluntarily share non-sensitive input state

What $4 one-time adds

subsequent base-rate counterfactual dial App calculations through a browser-local convenience signal
boundary-preserving print and row-and-summary CSV of this base-rate counterfactual dial; conditional retyping budget uses only your entered minutes
five local headline/count/time summaries, separately deletable; full-input draft is separate and not a backup

HYPOTHESIS / PROTOTYPE: the proposed $4 one-time price concerns filing this base-rate counterfactual dial, not obtaining its answer. No checkout is available. An optional manual-retyping baseline computes a conditional one-export retyping budget; preparation, review, measured savings and buyer demand are not implied.

Local history (this device)

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Optional filing controls are a local prototype.

Checkout is unavailable. The reading above is complete; print, CSV and five local summaries are optional enhancements, not hidden answers.

Before using the base-rate counterfactual dial

This is a classroom probability exercise using fictional detector codes, not a health, hiring, policing, credit or real-person screening tool. Sensitivity means probability of an alert given the positive class; specificity means probability of no alert given the negative class. Prevalence describes the assumed positive-class share of the modeled population. These quantities must refer to one compatible population and definition. The dial computes expected counts for a reference 10,000 cases only to make denominators visible. It neither observes individuals nor validates prevalence, operating conditions or a model’s calibration. A probability result should not be repurposed as advice about any actual person.

Boundary and sources

Declared local method: For prevalence p, sensitivity s and specificity c as fractions, expected true positives per 10,000 are 10,000ps and expected false positives are 10,000(1−p)(1−c). Divide true positives by their sum for positive predictive value. Also show false negatives, true negatives and negative predictive value where its denominator exists. The headline is the maximum defined positive predictive value across rows; the individual modeled cells remain next to each row, with no hidden premium explanation.
Every sample code, measurement, date, price, fingerprint and scenario is invented. Artifact checks do not verify reader data, policies, actual files, tickets, votes or health/accessibility outcomes.
Google’s official Gemini pricing page, fetched 2026-10-01, lists AI Studio access in its Free section, limited model access and free input/output tokens. Free-tier content may be used to improve products. Optional external formatting may require an account; limits/access can change. Manual local entry needs none. Do not send sensitive records.

Mechanism: competence-autonomy-loop

Optional AI formatting, never the calculation

Manual entry completes this base-rate counterfactual dial for free without signup. If available to you, the free AI Studio interface linked in the sources may format fictional or non-sensitive notes; external access may require an account. No API key or AI call is built into this tool. Free-tier content may be used to improve products. Review each cell and transcribe it to the labeled row schema; do not paste the JSON object into the row box.

Format only these fictional or non-sensitive notes for a base-rate counterfactual dial. Return strict JSON shaped as {"rows": [{"label": "string", "cells": ["string", "string"]}], "setting": "string"}. The columns are Detector code | sensitivity percent | specificity percent; the setting is Assumed positive-class prevalence (%). Keep all supplied strings and quantities exactly; do not calculate, infer missing entries, invent dates or add advice. If any required value is missing, return an empty rows array and ask me for it separately. I will verify every cell against my source and manually transcribe rows using vertical bars before running the local calculator.

An AI response is not executed, fetched or trusted as a result. Missing values remain questions; the strict local parser checks the rows you actually enter.